Does Size Matter? Utility of Size of Tuberculin Reactions for the Diagnosis of Mycobacterial Disease
Bibliographic record
Abstract
It is a common belief that larger tuberculin reactions are more serious, and more likely to indicate patients with active tuberculosis (TB) or at high risk of disease in the future. Among 182 close contacts, and 502 patients suspected of possible active TB, 529 underwent tuberculin skin testing (TST) and 605 had a chest radiograph. Final diagnoses, based on all available clinical, microbiological, histological, and radiographic information, were active TB, 68; inactive TB, 274; nontuberculous mycobacterial disease, 14; conditions associated with anergy, 36; no detectable abnormality (except a positive TST) or condition unrelated to TB, 213; and negative TST, no further evaluation, 79. Among these patients, TST of 5 mm or larger was significantly more likely to indicate active or inactive TB (p < 0.001). However, among patients with TST of 5 mm or greater, the size and frequency distribution of tuberculin reactions were not different between subjects with different diagnoses, nor between subjects with different types or extent of radiographic findings. As well, TST reactions were no different in 121 subjects with or 176 subjects without a history of BCG vaccination. In close contacts or patients suspected of active TB, reactions less than 5 mm indicated lower likelihood of active or inactive disease, but above that threshold, size of tuberculin reaction did not matter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".